{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-x-x2hhlOKkl"
   },
   "source": [
    "# Violence Detection using CNN + LSTM neural network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the increasing of surveillance cameras in modern cities, huge amount of videos can be collected. While there are insufficient human resource for monitoring all the screens at one time.\n",
    "\n",
    "We are considering how to use techniques of video understanding to detect violent behavior so that it can give a quick alarm in time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L8w5vbqMObaw"
   },
   "source": [
    "## Flowchart"
   ]
  },
  {
   "attachments": {
    "flowchart.JPG": {
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    }
   },
   "cell_type": "markdown",
   "metadata": {
    "id": "6kz7yE3kOgMk"
   },
   "source": [
    "![flowchart.JPG](attachment:flowchart.JPG)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UVXa7uMnOlkp"
   },
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "oDfDnlliPMd-",
    "outputId": "7582c7ed-2ba2-4164-e043-d0c8d31ba19f"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import cv2\n",
    "import os\n",
    "import numpy as np\n",
    "import keras\n",
    "import matplotlib.pyplot as plt\n",
    "from random import shuffle\n",
    "from tensorflow.keras.applications import VGG16\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.models import Model, Sequential\n",
    "from tensorflow.keras.layers import Input\n",
    "from tensorflow.keras.layers import LSTM\n",
    "from tensorflow.keras.layers import Dense, Activation , Dropout\n",
    "import sys\n",
    "import h5py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v6pf1l28PzIO"
   },
   "source": [
    "## Helper Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WKnyJkf8PzxE"
   },
   "source": [
    "We will use the function ```print_progress``` to print the amount of videos processed the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "qnafWmS7P3CG"
   },
   "outputs": [],
   "source": [
    "def print_progress(count, max_count):\n",
    "    # Percentage completion.\n",
    "    pct_complete = count / max_count\n",
    "\n",
    "    # Status-message. Note the \\r which means the line should\n",
    "    # overwrite itself.\n",
    "    msg = \"\\r- Progress: {0:.1%}\".format(pct_complete)\n",
    "\n",
    "    # Print it.\n",
    "    sys.stdout.write(msg)\n",
    "    sys.stdout.flush()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tRf-KgkjP9Kt"
   },
   "source": [
    "## Load Data\n",
    "\n",
    "Firstly, we define the directory to place the video dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "RiRKgwBgP-NY"
   },
   "outputs": [],
   "source": [
    "in_dir = \"../input/fight-detection-cctv/data\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UCZHrpJjRKky"
   },
   "source": [
    "Copy some of the data-dimensions for convenience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "SXTNEj6SRLZZ"
   },
   "outputs": [],
   "source": [
    "# Frame size  \n",
    "img_size = 224\n",
    "\n",
    "img_size_touple = (img_size, img_size)\n",
    "\n",
    "# Number of channels (RGB)\n",
    "num_channels = 3\n",
    "\n",
    "# Flat frame size\n",
    "img_size_flat = img_size * img_size * num_channels\n",
    "\n",
    "# Number of classes for classification (Violence-No Violence)\n",
    "num_classes = 2\n",
    "\n",
    "# Number of files to train\n",
    "_num_files_train = 1\n",
    "\n",
    "# Number of frames per video\n",
    "_images_per_file = 100\n",
    "\n",
    "# Number of frames per training set\n",
    "_num_images_train = _num_files_train * _images_per_file\n",
    "\n",
    "# Video extension\n",
    "video_exts = \".avi\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Wodq7EaSRSS8"
   },
   "source": [
    "### Helper-function for getting video frames\n",
    "Function used to get 100 frames from a video file and convert the frame to a suitable format for the neural net."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "eu9c4a-3RVkO"
   },
   "outputs": [],
   "source": [
    "def get_frames(current_dir, file_name):\n",
    "    \n",
    "    in_file = os.path.join(current_dir, file_name)\n",
    "    \n",
    "    images = []\n",
    "    \n",
    "    vidcap = cv2.VideoCapture(in_file)\n",
    "    \n",
    "    success,image = vidcap.read()\n",
    "        \n",
    "    count = 0\n",
    "\n",
    "    while count<_images_per_file:\n",
    "                \n",
    "        RGB_img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "    \n",
    "        res = cv2.resize(RGB_img, dsize=(img_size, img_size),\n",
    "                                 interpolation=cv2.INTER_CUBIC)\n",
    "    \n",
    "        images.append(res)\n",
    "    \n",
    "        success,image = vidcap.read()\n",
    "    \n",
    "        count += 1\n",
    "        \n",
    "    resul = np.array(images)\n",
    "    \n",
    "    resul = (resul / 255.).astype(np.float16)\n",
    "        \n",
    "    return resul"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tLCjYFBtRZb-"
   },
   "source": [
    "### Helper function to get the names of the data downloaded and label it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "Qiv5NIJjRbIA"
   },
   "outputs": [],
   "source": [
    "def label_video_names(in_dir):\n",
    "    \n",
    "    # list containing video names\n",
    "    names = []\n",
    "    # list containin video labels [1, 0] if it has violence and [0, 1] if not\n",
    "    labels = []\n",
    "    \n",
    "    \n",
    "    for current_dir, dir_names,file_names in os.walk(in_dir):\n",
    "        \n",
    "        for file_name in file_names:\n",
    "            \n",
    "            if file_name[0:2] == 'fi':\n",
    "                labels.append([1,0])\n",
    "                names.append(file_name)\n",
    "            elif file_name[0:2] == 'no':\n",
    "                labels.append([0,1])\n",
    "                names.append(file_name)\n",
    "                     \n",
    "            \n",
    "    c = list(zip(names,labels))\n",
    "    # Suffle the data (names and labels)\n",
    "    shuffle(c)\n",
    "    \n",
    "    names, labels = zip(*c)\n",
    "            \n",
    "    return names, labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t3KW2kfgReKn"
   },
   "source": [
    "### Plot a video frame to see if data is correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "dIsaAgcyRfIx"
   },
   "outputs": [],
   "source": [
    "# First get the names and labels of the whole videos\n",
    "names, labels = label_video_names(in_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EqucOMsJRgqm"
   },
   "source": [
    "Then we are going to load 100 frames of one video, for example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "xUfZO-0BRj0f",
    "outputId": "e2b913dc-c39f-4d5f-a0d6-da1273692fbc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'fi42.avi'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names[12]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ORGJ2pS9RnWw"
   },
   "source": [
    "The video has violence, look at the name of the video, starts with 'fi'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "EqBi8z6rRoMW"
   },
   "outputs": [],
   "source": [
    "frames = get_frames(in_dir, names[12])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vtgQEmI6RrmM"
   },
   "source": [
    "Convert back the frames to uint8 pixel format to plot the frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "9ihSA_ogRsNU"
   },
   "outputs": [],
   "source": [
    "visible_frame = (frames*255).astype('uint8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "PM1kNhaHRvSv",
    "outputId": "e2a6d324-cb62-42d2-dd8f-1cf1c464d168"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f621a03de90>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(visible_frame[3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sF1QieG5SANp"
   },
   "source": [
    "## Pre-Trained Model: VGG16"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0o6FA6slSA1l"
   },
   "source": [
    "The following creates an instance of the pre-trained VGG16 model using the Keras API. This automatically downloads the required files if you don't have them already.\n",
    "\n",
    "The VGG16 model contains a convolutional part and a fully-connected (or dense) part which is used for classification. If include_top=True then the whole VGG16 model is downloaded. If include_top=False then only the convolutional part of the VGG16 model is downloaded ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "MjRN6oE4SC81",
    "outputId": "1a9fbd16-9741-406d-b4fd-e3a20cd34a0d"
   },
   "outputs": [],
   "source": [
    "vid_model = VGG16(include_top=True, weights='imagenet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P_7z-y1mSPov"
   },
   "source": [
    "Let's see the model summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "ud7OU0t7SQPi",
    "outputId": "20718d3a-c1d3-4a06-d6f5-de3c465311df"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"vgg16\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 224, 224, 3)]     0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 25088)             0         \n",
      "_________________________________________________________________\n",
      "fc1 (Dense)                  (None, 4096)              102764544 \n",
      "_________________________________________________________________\n",
      "fc2 (Dense)                  (None, 4096)              16781312  \n",
      "_________________________________________________________________\n",
      "predictions (Dense)          (None, 1000)              4097000   \n",
      "=================================================================\n",
      "Total params: 138,357,544\n",
      "Trainable params: 138,357,544\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "vid_model.summary()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-NxnGLlDSTwr"
   },
   "source": [
    "We can observe the shape of the tensors expected as input by the pre-trained VGG16 model. In this case it is images of shape 224 x 224 x 3. Note that we have defined the frame size as 224x224x3. The video frame will be the input of the VGG16 net."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kMR6iqg4SaOl"
   },
   "source": [
    "### VGG16 model flowchart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qFUDqTiBSa0H"
   },
   "source": [
    "The following chart shows how the data flows when using the VGG16 model for Transfer Learning. First we input and process 100 video frames in batch with the VGG16 model. Just prior to the final classification layer of the VGG16 model, we save the so-called Transfer Values to a cache-file.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "4YWFA-2tSdfB",
    "outputId": "55dcb0c7-2b8a-4420-e25e-381152197a81"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The input of the VGG16 net have dimensions: (224, 224)\n",
      "The output of the selecter layer of VGG16 net have dimensions:  4096\n"
     ]
    }
   ],
   "source": [
    "# We will use the output of the layer prior to the final\n",
    "# classification-layer which is named fc2. This is a fully-connected (or dense) layer.\n",
    "transfer_layer = vid_model.get_layer('fc2')\n",
    "\n",
    "image_model_transfer = Model(inputs=vid_model.input,\n",
    "                             outputs=transfer_layer.output)\n",
    "\n",
    "transfer_values_size = K.int_shape(transfer_layer.output)[1]\n",
    "\n",
    "\n",
    "print(\"The input of the VGG16 net have dimensions:\",K.int_shape(vid_model.input)[1:3])\n",
    "\n",
    "print(\"The output of the selecter layer of VGG16 net have dimensions: \", transfer_values_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HGdRIG6oSooG"
   },
   "source": [
    "### Generator that process one video through VGG16 each function call"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "D2BvaY3eSpSH"
   },
   "outputs": [],
   "source": [
    "def proces_transfer(vid_names, in_dir, labels):\n",
    "    \n",
    "    count = 0\n",
    "    \n",
    "    tam = len(vid_names)\n",
    "    \n",
    "    # Pre-allocate input-batch-array for images.\n",
    "    shape = (_images_per_file,) + img_size_touple + (3,)\n",
    "    \n",
    "    while count<tam:\n",
    "        \n",
    "        video_name = vid_names[count]\n",
    "        \n",
    "        image_batch = np.zeros(shape=shape, dtype=np.float16)\n",
    "    \n",
    "        image_batch = get_frames(in_dir, video_name)\n",
    "        \n",
    "         # Note that we use 16-bit floating-points to save memory.\n",
    "        shape = (_images_per_file, transfer_values_size)\n",
    "        transfer_values = np.zeros(shape=shape, dtype=np.float16)\n",
    "        \n",
    "        transfer_values = \\\n",
    "            image_model_transfer.predict(image_batch)\n",
    "         \n",
    "        labels1 = labels[count]\n",
    "        \n",
    "        aux = np.ones([100,2])\n",
    "        \n",
    "        labelss = labels1*aux\n",
    "        \n",
    "        yield transfer_values, labelss\n",
    "        \n",
    "        count+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I-Cnv0fRSswF"
   },
   "source": [
    "### Functions to save transfer values from VGG16 to later use\n",
    "We are going to define functions to get the transfer values from VGG16 with defined number of files. Then save the transfer values files used from training in one file and the ones uses for testing in another one. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "tvU53ypSSvL0"
   },
   "outputs": [],
   "source": [
    "def make_files(n_files):\n",
    "    \n",
    "    gen = proces_transfer(names_training, in_dir, labels_training)\n",
    "\n",
    "    numer = 1\n",
    "\n",
    "    # Read the first chunk to get the column dtypes\n",
    "    chunk = next(gen)\n",
    "\n",
    "    row_count = chunk[0].shape[0]\n",
    "    row_count2 = chunk[1].shape[0]\n",
    "    \n",
    "    with h5py.File('prueba.h5', 'w') as f:\n",
    "    \n",
    "        # Initialize a resizable dataset to hold the output\n",
    "        maxshape = (None,) + chunk[0].shape[1:]\n",
    "        maxshape2 = (None,) + chunk[1].shape[1:]\n",
    "    \n",
    "    \n",
    "        dset = f.create_dataset('data', shape=chunk[0].shape, maxshape=maxshape,\n",
    "                                chunks=chunk[0].shape, dtype=chunk[0].dtype)\n",
    "    \n",
    "        dset2 = f.create_dataset('labels', shape=chunk[1].shape, maxshape=maxshape2,\n",
    "                                 chunks=chunk[1].shape, dtype=chunk[1].dtype)\n",
    "    \n",
    "         # Write the first chunk of rows\n",
    "        dset[:] = chunk[0]\n",
    "        dset2[:] = chunk[1]\n",
    "\n",
    "        for chunk in gen:\n",
    "            \n",
    "            if numer == n_files:\n",
    "            \n",
    "                break\n",
    "\n",
    "            # Resize the dataset to accommodate the next chunk of rows\n",
    "            dset.resize(row_count + chunk[0].shape[0], axis=0)\n",
    "            dset2.resize(row_count2 + chunk[1].shape[0], axis=0)\n",
    "\n",
    "            # Write the next chunk\n",
    "            dset[row_count:] = chunk[0]\n",
    "            dset2[row_count:] = chunk[1]\n",
    "\n",
    "            # Increment the row count\n",
    "            row_count += chunk[0].shape[0]\n",
    "            row_count2 += chunk[1].shape[0]\n",
    "            \n",
    "            print_progress(numer, n_files)\n",
    "        \n",
    "            numer += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "8nK8uFExS0nX"
   },
   "outputs": [],
   "source": [
    "def make_files_test(n_files):\n",
    "    \n",
    "    gen = proces_transfer(names_test, in_dir, labels_test)\n",
    "\n",
    "    numer = 1\n",
    "\n",
    "    # Read the first chunk to get the column dtypes\n",
    "    chunk = next(gen)\n",
    "\n",
    "    row_count = chunk[0].shape[0]\n",
    "    row_count2 = chunk[1].shape[0]\n",
    "    \n",
    "    with h5py.File('pruebavalidation.h5', 'w') as f:\n",
    "    \n",
    "        # Initialize a resizable dataset to hold the output\n",
    "        maxshape = (None,) + chunk[0].shape[1:]\n",
    "        maxshape2 = (None,) + chunk[1].shape[1:]\n",
    "    \n",
    "    \n",
    "        dset = f.create_dataset('data', shape=chunk[0].shape, maxshape=maxshape,\n",
    "                                chunks=chunk[0].shape, dtype=chunk[0].dtype)\n",
    "    \n",
    "        dset2 = f.create_dataset('labels', shape=chunk[1].shape, maxshape=maxshape2,\n",
    "                                 chunks=chunk[1].shape, dtype=chunk[1].dtype)\n",
    "    \n",
    "         # Write the first chunk of rows\n",
    "        dset[:] = chunk[0]\n",
    "        dset2[:] = chunk[1]\n",
    "\n",
    "        for chunk in gen:\n",
    "            \n",
    "            if numer == n_files:\n",
    "            \n",
    "                break\n",
    "\n",
    "            # Resize the dataset to accommodate the next chunk of rows\n",
    "            dset.resize(row_count + chunk[0].shape[0], axis=0)\n",
    "            dset2.resize(row_count2 + chunk[1].shape[0], axis=0)\n",
    "\n",
    "            # Write the next chunk\n",
    "            dset[row_count:] = chunk[0]\n",
    "            dset2[row_count:] = chunk[1]\n",
    "\n",
    "            # Increment the row count\n",
    "            row_count += chunk[0].shape[0]\n",
    "            row_count2 += chunk[1].shape[0]\n",
    "            \n",
    "            print_progress(numer, n_files)\n",
    "        \n",
    "            numer += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R9axnZ8dS64T"
   },
   "source": [
    "### Split the dataset into training set and test set\n",
    "We are going to split the dataset into training set and testing. The training set is used to train the model and the test set to check the model accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "wjne3svdS9Y3"
   },
   "outputs": [],
   "source": [
    "test_set = int(len(names)*0.2)\n",
    "\n",
    "names_training = names[0:training_set]\n",
    "names_test = names[training_set:]\n",
    "\n",
    "labels_training = labels[0:training_set]\n",
    "labels_test = labels[training_set:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xmiJi6G7TBnI"
   },
   "source": [
    "Then we are going to process all video frames through VGG16 and save the transfer values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "pyO9WP-6TER4",
    "outputId": "94209006-bfb9-470f-d418-cd2acfbdb12d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Progress: 99.9%"
     ]
    }
   ],
   "source": [
    "make_files(training_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "0ThoefXUXPrS",
    "outputId": "9f4cdc1e-4bfb-4ab6-81c1-05abadc1239f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Progress: 99.7%"
     ]
    }
   ],
   "source": [
    "make_files_test(test_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0s2imcRixMAr"
   },
   "source": [
    "### Load the cached transfer values into memory\n",
    "We have already saved all the videos transfer values into disk. But we have to load those transfer values into memory in order to train the LSTM net. One question would be: why not process transfer values and load them into RAM memory? Yes is a more eficient way to train the second net. But if you have to train the LSTM in different ways in order to see which way gets the best accuracy, if you didn't save the transfer values into disk you would have to process the whole videos each training. It's very time consuming processing the videos through VGG16 net. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RnK1JL-izzeS"
   },
   "source": [
    "In order to load the saved transfer values into RAM memory we are going to use this two functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "Ez0blP2z0CsF"
   },
   "outputs": [],
   "source": [
    "def process_alldata_training():\n",
    "    \n",
    "    joint_transfer=[]\n",
    "    frames_num=100\n",
    "    count = 0\n",
    "    \n",
    "    with h5py.File('prueba.h5', 'r') as f:\n",
    "            \n",
    "        X_batch = f['data'][:]\n",
    "        y_batch = f['labels'][:]\n",
    "\n",
    "    for i in range(int(len(X_batch)/frames_num)):\n",
    "        inc = count+frames_num\n",
    "        joint_transfer.append([X_batch[count:inc],y_batch[count]])\n",
    "        count =inc\n",
    "        \n",
    "    data =[]\n",
    "    target=[]\n",
    "    \n",
    "    for i in joint_transfer:\n",
    "        data.append(i[0])\n",
    "        target.append(np.array(i[1]))\n",
    "        \n",
    "    return data, target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "Dne2XaQ90MGk"
   },
   "outputs": [],
   "source": [
    "def process_alldata_test():\n",
    "    \n",
    "    joint_transfer=[]\n",
    "    frames_num=100\n",
    "    count = 0\n",
    "    \n",
    "    with h5py.File('pruebavalidation.h5', 'r') as f:\n",
    "            \n",
    "        X_batch = f['data'][:]\n",
    "        y_batch = f['labels'][:]\n",
    "\n",
    "    for i in range(int(len(X_batch)/frames_num)):\n",
    "        inc = count+frames_num\n",
    "        joint_transfer.append([X_batch[count:inc],y_batch[count]])\n",
    "        count =inc\n",
    "        \n",
    "    data =[]\n",
    "    target=[]\n",
    "    \n",
    "    for i in joint_transfer:\n",
    "        data.append(i[0])\n",
    "        target.append(np.array(i[1]))\n",
    "        \n",
    "    return data, target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "phyhoYc67VW8"
   },
   "outputs": [],
   "source": [
    "data, target = process_alldata_training()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "dJpXHvEg7Xhc"
   },
   "outputs": [],
   "source": [
    "data_test, target_test = process_alldata_test()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Qjaawyqj0arq"
   },
   "source": [
    "## Recurrent Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BsIrL3ix4Edp"
   },
   "source": [
    "### Define LSTM architecture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qFiSjMaC4Q1c"
   },
   "source": [
    "When defining the LSTM architecture we have to take into account the dimensions of the transfer values. From each frame the VGG16 network obtains as output a vector of 4096 transfer values. From each video we are processing 100 frames so we will have 100 x 4096 values per video. The classification must be done taking into account the 100 frames of the video. If any of them detects violence, the video will be classified as violent.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HAgSUeVu58N_"
   },
   "source": [
    "The first input dimension of LSTM neurons is the temporal dimension, in our case it is 100. The second is the size of the features vector (transfer values).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "id": "XWABZ91b6f7l"
   },
   "outputs": [],
   "source": [
    "chunk_size = 4096\n",
    "n_chunks = 100\n",
    "rnn_size = 512\n",
    "\n",
    "model = Sequential()\n",
    "model.add(LSTM(rnn_size, input_shape=(n_chunks, chunk_size)))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(512))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(50))\n",
    "model.add(Activation('sigmoid'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(2))\n",
    "model.add(Activation('softmax'))\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bVonOPYW7F_b"
   },
   "source": [
    "## Model training\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "id": "iRZlW4ZV_ygS",
    "outputId": "c223a55f-8ccd-4e5a-f5d4-dc6a1daf2d0e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "3/3 - 5s - loss: 0.7555 - accuracy: 0.5067 - val_loss: 0.6975 - val_accuracy: 0.5125\n",
      "Epoch 2/100\n",
      "3/3 - 2s - loss: 0.7397 - accuracy: 0.4842 - val_loss: 0.6912 - val_accuracy: 0.5125\n",
      "Epoch 3/100\n",
      "3/3 - 2s - loss: 0.7241 - accuracy: 0.4842 - val_loss: 0.6938 - val_accuracy: 0.4875\n",
      "Epoch 4/100\n",
      "3/3 - 2s - loss: 0.7097 - accuracy: 0.4958 - val_loss: 0.6909 - val_accuracy: 0.5125\n",
      "Epoch 5/100\n",
      "3/3 - 2s - loss: 0.7079 - accuracy: 0.4950 - val_loss: 0.6902 - val_accuracy: 0.6750\n",
      "Epoch 6/100\n",
      "3/3 - 2s - loss: 0.6996 - accuracy: 0.5092 - val_loss: 0.6904 - val_accuracy: 0.4875\n",
      "Epoch 7/100\n",
      "3/3 - 2s - loss: 0.6970 - accuracy: 0.5158 - val_loss: 0.6910 - val_accuracy: 0.4875\n",
      "Epoch 8/100\n",
      "3/3 - 2s - loss: 0.6971 - accuracy: 0.4942 - val_loss: 0.6904 - val_accuracy: 0.4875\n",
      "Epoch 9/100\n",
      "3/3 - 2s - loss: 0.6945 - accuracy: 0.5225 - val_loss: 0.6898 - val_accuracy: 0.6125\n",
      "Epoch 10/100\n",
      "3/3 - 2s - loss: 0.6932 - accuracy: 0.4967 - val_loss: 0.6894 - val_accuracy: 0.7250\n",
      "Epoch 11/100\n",
      "3/3 - 2s - loss: 0.6885 - accuracy: 0.5442 - val_loss: 0.6886 - val_accuracy: 0.6375\n",
      "Epoch 12/100\n",
      "3/3 - 2s - loss: 0.6941 - accuracy: 0.5200 - val_loss: 0.6879 - val_accuracy: 0.5125\n",
      "Epoch 13/100\n",
      "3/3 - 2s - loss: 0.6908 - accuracy: 0.5242 - val_loss: 0.6877 - val_accuracy: 0.5750\n",
      "Epoch 14/100\n",
      "3/3 - 2s - loss: 0.6901 - accuracy: 0.5350 - val_loss: 0.6861 - val_accuracy: 0.6250\n",
      "Epoch 15/100\n",
      "3/3 - 2s - loss: 0.6898 - accuracy: 0.5392 - val_loss: 0.6845 - val_accuracy: 0.6250\n",
      "Epoch 16/100\n",
      "3/3 - 2s - loss: 0.6873 - accuracy: 0.5508 - val_loss: 0.6824 - val_accuracy: 0.7125\n",
      "Epoch 17/100\n",
      "3/3 - 2s - loss: 0.6860 - accuracy: 0.5683 - val_loss: 0.6775 - val_accuracy: 0.7125\n",
      "Epoch 18/100\n",
      "3/3 - 2s - loss: 0.6820 - accuracy: 0.5825 - val_loss: 0.6724 - val_accuracy: 0.7000\n",
      "Epoch 19/100\n",
      "3/3 - 2s - loss: 0.6809 - accuracy: 0.6000 - val_loss: 0.6649 - val_accuracy: 0.7000\n",
      "Epoch 20/100\n",
      "3/3 - 2s - loss: 0.6755 - accuracy: 0.5692 - val_loss: 0.6544 - val_accuracy: 0.6750\n",
      "Epoch 21/100\n",
      "3/3 - 2s - loss: 0.6698 - accuracy: 0.5983 - val_loss: 0.6427 - val_accuracy: 0.7125\n",
      "Epoch 22/100\n",
      "3/3 - 2s - loss: 0.6648 - accuracy: 0.6042 - val_loss: 0.6211 - val_accuracy: 0.7500\n",
      "Epoch 23/100\n",
      "3/3 - 2s - loss: 0.6537 - accuracy: 0.6417 - val_loss: 0.6019 - val_accuracy: 0.7125\n",
      "Epoch 24/100\n",
      "3/3 - 2s - loss: 0.6525 - accuracy: 0.6167 - val_loss: 0.5849 - val_accuracy: 0.7625\n",
      "Epoch 25/100\n",
      "3/3 - 2s - loss: 0.6345 - accuracy: 0.6625 - val_loss: 0.5671 - val_accuracy: 0.7625\n",
      "Epoch 26/100\n",
      "3/3 - 2s - loss: 0.6427 - accuracy: 0.6283 - val_loss: 0.5700 - val_accuracy: 0.7125\n",
      "Epoch 27/100\n",
      "3/3 - 2s - loss: 0.6337 - accuracy: 0.6483 - val_loss: 0.5528 - val_accuracy: 0.7625\n",
      "Epoch 28/100\n",
      "3/3 - 2s - loss: 0.6268 - accuracy: 0.6608 - val_loss: 0.5644 - val_accuracy: 0.7125\n",
      "Epoch 29/100\n",
      "3/3 - 2s - loss: 0.6144 - accuracy: 0.6792 - val_loss: 0.5434 - val_accuracy: 0.7250\n",
      "Epoch 30/100\n",
      "3/3 - 2s - loss: 0.6201 - accuracy: 0.6617 - val_loss: 0.5369 - val_accuracy: 0.7625\n",
      "Epoch 31/100\n",
      "3/3 - 2s - loss: 0.6254 - accuracy: 0.6633 - val_loss: 0.5566 - val_accuracy: 0.7250\n",
      "Epoch 32/100\n",
      "3/3 - 2s - loss: 0.6180 - accuracy: 0.6750 - val_loss: 0.5531 - val_accuracy: 0.6875\n",
      "Epoch 33/100\n",
      "3/3 - 2s - loss: 0.6242 - accuracy: 0.6692 - val_loss: 0.5717 - val_accuracy: 0.7000\n",
      "Epoch 34/100\n",
      "3/3 - 2s - loss: 0.6009 - accuracy: 0.7025 - val_loss: 0.5504 - val_accuracy: 0.7125\n",
      "Epoch 35/100\n",
      "3/3 - 2s - loss: 0.6264 - accuracy: 0.6675 - val_loss: 0.6270 - val_accuracy: 0.6500\n",
      "Epoch 36/100\n",
      "3/3 - 2s - loss: 0.6719 - accuracy: 0.6133 - val_loss: 0.5816 - val_accuracy: 0.7000\n",
      "Epoch 37/100\n",
      "3/3 - 2s - loss: 0.6340 - accuracy: 0.6433 - val_loss: 0.5632 - val_accuracy: 0.6875\n",
      "Epoch 38/100\n",
      "3/3 - 2s - loss: 0.6268 - accuracy: 0.6467 - val_loss: 0.5962 - val_accuracy: 0.7000\n",
      "Epoch 39/100\n",
      "3/3 - 2s - loss: 0.6211 - accuracy: 0.6725 - val_loss: 0.5522 - val_accuracy: 0.7625\n",
      "Epoch 40/100\n",
      "3/3 - 2s - loss: 0.6179 - accuracy: 0.6617 - val_loss: 0.5564 - val_accuracy: 0.7375\n",
      "Epoch 41/100\n",
      "3/3 - 2s - loss: 0.6089 - accuracy: 0.6842 - val_loss: 0.5901 - val_accuracy: 0.6750\n",
      "Epoch 42/100\n",
      "3/3 - 2s - loss: 0.6083 - accuracy: 0.6842 - val_loss: 0.5476 - val_accuracy: 0.7125\n",
      "Epoch 43/100\n",
      "3/3 - 2s - loss: 0.6175 - accuracy: 0.6750 - val_loss: 0.5589 - val_accuracy: 0.7000\n",
      "Epoch 44/100\n",
      "3/3 - 2s - loss: 0.6053 - accuracy: 0.6867 - val_loss: 0.5458 - val_accuracy: 0.7375\n",
      "Epoch 45/100\n",
      "3/3 - 2s - loss: 0.5908 - accuracy: 0.6825 - val_loss: 0.5287 - val_accuracy: 0.7250\n",
      "Epoch 46/100\n",
      "3/3 - 2s - loss: 0.5960 - accuracy: 0.6875 - val_loss: 0.5571 - val_accuracy: 0.7000\n",
      "Epoch 47/100\n",
      "3/3 - 2s - loss: 0.5844 - accuracy: 0.6950 - val_loss: 0.5197 - val_accuracy: 0.7500\n",
      "Epoch 48/100\n",
      "3/3 - 2s - loss: 0.5853 - accuracy: 0.7033 - val_loss: 0.5393 - val_accuracy: 0.7375\n",
      "Epoch 49/100\n",
      "3/3 - 2s - loss: 0.5942 - accuracy: 0.6933 - val_loss: 0.5290 - val_accuracy: 0.7500\n",
      "Epoch 50/100\n",
      "3/3 - 2s - loss: 0.5914 - accuracy: 0.7000 - val_loss: 0.5300 - val_accuracy: 0.7375\n",
      "Epoch 51/100\n",
      "3/3 - 2s - loss: 0.5855 - accuracy: 0.7042 - val_loss: 0.5204 - val_accuracy: 0.7375\n",
      "Epoch 52/100\n",
      "3/3 - 2s - loss: 0.5920 - accuracy: 0.6958 - val_loss: 0.5468 - val_accuracy: 0.7125\n",
      "Epoch 53/100\n",
      "3/3 - 2s - loss: 0.5631 - accuracy: 0.7233 - val_loss: 0.5181 - val_accuracy: 0.7625\n",
      "Epoch 54/100\n",
      "3/3 - 2s - loss: 0.5769 - accuracy: 0.7133 - val_loss: 0.5331 - val_accuracy: 0.7125\n",
      "Epoch 55/100\n",
      "3/3 - 2s - loss: 0.5707 - accuracy: 0.7150 - val_loss: 0.5183 - val_accuracy: 0.7500\n",
      "Epoch 56/100\n",
      "3/3 - 2s - loss: 0.5967 - accuracy: 0.7125 - val_loss: 0.5466 - val_accuracy: 0.7000\n",
      "Epoch 57/100\n",
      "3/3 - 2s - loss: 0.5876 - accuracy: 0.7058 - val_loss: 0.5228 - val_accuracy: 0.7375\n",
      "Epoch 58/100\n",
      "3/3 - 2s - loss: 0.5726 - accuracy: 0.7117 - val_loss: 0.5460 - val_accuracy: 0.7000\n",
      "Epoch 59/100\n",
      "3/3 - 2s - loss: 0.5807 - accuracy: 0.7142 - val_loss: 0.5279 - val_accuracy: 0.7250\n",
      "Epoch 60/100\n",
      "3/3 - 2s - loss: 0.5801 - accuracy: 0.7083 - val_loss: 0.5493 - val_accuracy: 0.6875\n",
      "Epoch 61/100\n",
      "3/3 - 2s - loss: 0.5683 - accuracy: 0.7183 - val_loss: 0.5174 - val_accuracy: 0.7500\n",
      "Epoch 62/100\n",
      "3/3 - 2s - loss: 0.5788 - accuracy: 0.7083 - val_loss: 0.5390 - val_accuracy: 0.7000\n",
      "Epoch 63/100\n",
      "3/3 - 2s - loss: 0.5637 - accuracy: 0.7292 - val_loss: 0.5332 - val_accuracy: 0.7250\n",
      "Epoch 64/100\n",
      "3/3 - 2s - loss: 0.5715 - accuracy: 0.7150 - val_loss: 0.5294 - val_accuracy: 0.7125\n",
      "Epoch 65/100\n",
      "3/3 - 2s - loss: 0.5732 - accuracy: 0.7250 - val_loss: 0.5339 - val_accuracy: 0.7125\n",
      "Epoch 66/100\n",
      "3/3 - 2s - loss: 0.5620 - accuracy: 0.7208 - val_loss: 0.5289 - val_accuracy: 0.7375\n",
      "Epoch 67/100\n",
      "3/3 - 2s - loss: 0.5636 - accuracy: 0.7317 - val_loss: 0.5508 - val_accuracy: 0.6875\n",
      "Epoch 68/100\n",
      "3/3 - 2s - loss: 0.5664 - accuracy: 0.7242 - val_loss: 0.5258 - val_accuracy: 0.7375\n",
      "Epoch 69/100\n",
      "3/3 - 2s - loss: 0.5699 - accuracy: 0.7308 - val_loss: 0.5473 - val_accuracy: 0.7000\n",
      "Epoch 70/100\n",
      "3/3 - 2s - loss: 0.5645 - accuracy: 0.7208 - val_loss: 0.5226 - val_accuracy: 0.7250\n",
      "Epoch 71/100\n",
      "3/3 - 2s - loss: 0.5629 - accuracy: 0.7300 - val_loss: 0.5497 - val_accuracy: 0.6875\n",
      "Epoch 72/100\n",
      "3/3 - 2s - loss: 0.5796 - accuracy: 0.7125 - val_loss: 0.5267 - val_accuracy: 0.7250\n",
      "Epoch 73/100\n",
      "3/3 - 2s - loss: 0.5659 - accuracy: 0.7350 - val_loss: 0.5391 - val_accuracy: 0.7125\n",
      "Epoch 74/100\n",
      "3/3 - 2s - loss: 0.5637 - accuracy: 0.7267 - val_loss: 0.5348 - val_accuracy: 0.7125\n",
      "Epoch 75/100\n",
      "3/3 - 2s - loss: 0.5462 - accuracy: 0.7425 - val_loss: 0.5438 - val_accuracy: 0.7125\n",
      "Epoch 76/100\n",
      "3/3 - 2s - loss: 0.5570 - accuracy: 0.7425 - val_loss: 0.5287 - val_accuracy: 0.7500\n",
      "Epoch 77/100\n",
      "3/3 - 2s - loss: 0.5530 - accuracy: 0.7483 - val_loss: 0.5604 - val_accuracy: 0.6750\n",
      "Epoch 78/100\n",
      "3/3 - 2s - loss: 0.5538 - accuracy: 0.7308 - val_loss: 0.5314 - val_accuracy: 0.7625\n",
      "Epoch 79/100\n",
      "3/3 - 2s - loss: 0.5627 - accuracy: 0.7300 - val_loss: 0.5581 - val_accuracy: 0.6875\n",
      "Epoch 80/100\n",
      "3/3 - 2s - loss: 0.5438 - accuracy: 0.7325 - val_loss: 0.5295 - val_accuracy: 0.7375\n",
      "Epoch 81/100\n",
      "3/3 - 2s - loss: 0.5400 - accuracy: 0.7392 - val_loss: 0.5396 - val_accuracy: 0.7125\n",
      "Epoch 82/100\n",
      "3/3 - 2s - loss: 0.5524 - accuracy: 0.7392 - val_loss: 0.5421 - val_accuracy: 0.7125\n",
      "Epoch 83/100\n",
      "3/3 - 2s - loss: 0.5349 - accuracy: 0.7433 - val_loss: 0.5308 - val_accuracy: 0.7375\n",
      "Epoch 84/100\n",
      "3/3 - 2s - loss: 0.5480 - accuracy: 0.7442 - val_loss: 0.5705 - val_accuracy: 0.6750\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 85/100\n",
      "3/3 - 2s - loss: 0.5483 - accuracy: 0.7375 - val_loss: 0.5342 - val_accuracy: 0.7375\n",
      "Epoch 86/100\n",
      "3/3 - 2s - loss: 0.5521 - accuracy: 0.7300 - val_loss: 0.5530 - val_accuracy: 0.6875\n",
      "Epoch 87/100\n",
      "3/3 - 2s - loss: 0.5541 - accuracy: 0.7233 - val_loss: 0.5492 - val_accuracy: 0.7125\n",
      "Epoch 88/100\n",
      "3/3 - 2s - loss: 0.5614 - accuracy: 0.7142 - val_loss: 0.5581 - val_accuracy: 0.6750\n",
      "Epoch 89/100\n",
      "3/3 - 2s - loss: 0.5701 - accuracy: 0.7217 - val_loss: 0.5542 - val_accuracy: 0.6750\n",
      "Epoch 90/100\n",
      "3/3 - 2s - loss: 0.5663 - accuracy: 0.7125 - val_loss: 0.5535 - val_accuracy: 0.6750\n",
      "Epoch 91/100\n",
      "3/3 - 2s - loss: 0.5475 - accuracy: 0.7300 - val_loss: 0.5377 - val_accuracy: 0.7125\n",
      "Epoch 92/100\n",
      "3/3 - 2s - loss: 0.5467 - accuracy: 0.7333 - val_loss: 0.5565 - val_accuracy: 0.6625\n",
      "Epoch 93/100\n",
      "3/3 - 2s - loss: 0.5357 - accuracy: 0.7442 - val_loss: 0.5422 - val_accuracy: 0.7375\n",
      "Epoch 94/100\n",
      "3/3 - 2s - loss: 0.5523 - accuracy: 0.7375 - val_loss: 0.5830 - val_accuracy: 0.6875\n",
      "Epoch 95/100\n",
      "3/3 - 2s - loss: 0.5641 - accuracy: 0.7100 - val_loss: 0.5417 - val_accuracy: 0.7375\n",
      "Epoch 96/100\n",
      "3/3 - 2s - loss: 0.5675 - accuracy: 0.7183 - val_loss: 0.5511 - val_accuracy: 0.6750\n",
      "Epoch 97/100\n",
      "3/3 - 2s - loss: 0.5599 - accuracy: 0.7258 - val_loss: 0.5378 - val_accuracy: 0.7000\n",
      "Epoch 98/100\n",
      "3/3 - 2s - loss: 0.5493 - accuracy: 0.7325 - val_loss: 0.5363 - val_accuracy: 0.7250\n",
      "Epoch 99/100\n",
      "3/3 - 2s - loss: 0.5635 - accuracy: 0.7300 - val_loss: 0.5531 - val_accuracy: 0.6750\n",
      "Epoch 100/100\n",
      "3/3 - 2s - loss: 0.5369 - accuracy: 0.7458 - val_loss: 0.5341 - val_accuracy: 0.7625\n"
     ]
    }
   ],
   "source": [
    "epoch = 100\n",
    "batchS = 500\n",
    "\n",
    "history = model.fit(np.array(data[0:1200]), np.array(target[0:1200]), epochs=epoch,\n",
    "                    validation_data=(np.array(data[1200:]), np.array(target[1200:])), \n",
    "                    batch_size=batchS, verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BVJCgz3bA4A3"
   },
   "source": [
    "## Test the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3MwCq0LpA59G"
   },
   "source": [
    "We are going to test the model with 20 % of the total videos. This videos have not been used to train the network. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "id": "VDXFFy6zBG3X",
    "outputId": "6ac389f9-6400-401d-dfe7-c3e90ce8a0e4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 1s 42ms/step - loss: 0.6189 - accuracy: 0.7031\n"
     ]
    }
   ],
   "source": [
    "result = model.evaluate(np.array(data_test), np.array(target_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8YG-bPW4BL6U"
   },
   "source": [
    "## Print the model accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "id": "wBV2t2Q6BOt9",
    "outputId": "1dadcb49-b517-4cdc-9624-cb76c529b457"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss 0.6188818216323853\n",
      "accuracy 0.703125\n"
     ]
    }
   ],
   "source": [
    "for name, value in zip(model.metrics_names, result):\n",
    "    print(name, value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "id": "zO6YSbxgBZ3f",
    "outputId": "f8a99f0c-2cf2-4228-f6ac-9083c39443fb"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'validation'], loc='upper left')\n",
    "plt.savefig('destination_path.eps', format='eps', dpi=1000)\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'validation'], loc='upper left')\n",
    "plt.savefig('destination_path1.eps', format='eps', dpi=1000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problems\n",
    "\n",
    "Since all the videos are captured by surveillance cameras in public places, many of them may not have a good imaging quality due to dark environment, fast movement of object, lighting blur, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### recommendation\n",
    "\n",
    "I did not use all the data that I have because to train this model on a larger amount of data than this one needs super machine, and when training the model I noticed that the more data the amount >>> the more accurate the model.\n",
    "That is why it would be great to use a larger amount of data with a super computer\n",
    "\n",
    "This is why i see that the 70% accuracy is a satisfactory result ."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
